Introduction

Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs.We present a novel pattern-based probabilistic approach, PSSV, to identify somatic structural variations from WGS data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with heterozygous mutations in normal samples and homozygous mutations in tumor samples. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development.An R package of PSSV is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Publications

  1. PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification.
    Cite this
    Chen X, Shi X, Hilakivi-Clarke L, Shajahan-Haq AN, Clarke R, Xuan J, 2017-01-01 - Bioinformatics (Oxford, England)

Credits

  1. Xi Chen
    Developer

    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, United States of America

  2. Xu Shi
    Developer

    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, United States of America

  3. Leena Hilakivi-Clarke
    Developer

    Department of Oncology, Lombardi Comprehensive Cancer Center, United States of America

  4. Ayesha N Shajahan-Haq
    Developer

    Department of Oncology, Lombardi Comprehensive Cancer Center, United States of America

  5. Robert Clarke
    Developer

    Department of Oncology, Lombardi Comprehensive Cancer Center, United States of America

  6. Jianhua Xuan
    Investigator

    Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, United States of America

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Summary
AccessionBT002350
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesPerl, R
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByJianhua Xuan